William greene stern school of business new york university
Download
1 / 34

Efficiency Measurement - PowerPoint PPT Presentation


  • 101 Views
  • Updated On :

William Greene Stern School of Business New York University. Efficiency Measurement. Lab Session 2. Stochastic Frontier Estimation. Application to Spanish Dairy Farms. N = 247 farms, T = 6 years (1993-1998). Using Farm Means of the Data. OLS vs. Frontier/MLE. JLMS Inefficiency Estimator.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Efficiency Measurement' - channer


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
William greene stern school of business new york university l.jpg

William Greene

Stern School of Business

New York University

Efficiency Measurement


Lab session 2 l.jpg

Lab Session 2

Stochastic Frontier Estimation


Application to spanish dairy farms l.jpg
Application to Spanish Dairy Farms

N = 247 farms, T = 6 years (1993-1998)




Jlms inefficiency estimator l.jpg
JLMS Inefficiency Estimator

FRONTIER ; LHS = the variable

; RHS = ONE, the variables

; EFF = the new variable $

Creates a new variable in the data set.

FRONTIER ; LHS = YIT ; RHS = X ; EFF = U_i $

Use ;Techeff = variable to compute exp(-u).






Similar but different with a crucial pattern l.jpg
Similar, but different u(i)with a crucial pattern






Cost frontier command l.jpg
Cost Frontier Command u(i)

FRONTIER ; COST

; LHS = the variable

; RHS = ONE, the variables

; EFF = the new variable $

ε(i) = v(i) + u(i) [u(i) is still positive]




Normal truncated normal frontier command l.jpg
Normal-Truncated Normal u(i)Frontier Command

FRONTIER [; COST]

; LHS = the variable

; RHS = ONE, the variables

; Model = Truncation

; EFF = the new variable $

ε(i) = v(i) +/- u(i)

u(i) = |U(i)|, U(i) ~ N[μ,2]

The half normal model has μ = 0.


Observations l.jpg
Observations u(i)

  • Truncation Model estimation is often unstable

    • Often estimation is not possible

    • When possible, estimates are often wild

  • Estimates of u(i) are usually only moderately affected

  • Estimates of u(i) are fairly stable across models (exponential, truncation, etc.)


Truncated normal model model t l.jpg
Truncated Normal Model u(i) ; Model = T




Ranking observations l.jpg
Ranking Observations u(i)

CREATE ; newname = Rnk ( Variable ) $

Creates the set of ranks. Use in any subsequent analysis.


ad